| NiA |
18 |
| NA |
3 |
| LDA |
2 |
| N/A |
2 |
| NLP |
2 |
| nia |
2 |
| search engine |
2 |
| 11 different classifiers |
1 |
| collocations) |
1 |
| dictionary |
1 |
| sentiment analysis (unspecified in abstract) |
1 |
| AEDA, LaBSE, BiLSTM |
1 |
| Classifier |
1 |
| Deep learning sentiment analysis based on a autoregressive language model |
1 |
| LDA + sentiment analysis (unspecified how) |
1 |
| Lexicon and supervised machine learning |
1 |
| Machine learning |
1 |
| Metaheuristics based long short term memory |
1 |
| Natural Language Tool |
1 |
| Not stated |
1 |
| Opinion Mining approach with unclear approach |
1 |
| Relational Network approach |
1 |
| SML, but unclear what type |
1 |
| Sentiment Analysis |
1 |
| Unclear |
1 |
| a new model for zero-shot stance detection |
1 |
| adaptive learning emotion identification method (ALEIM) based on mutual information feature weight |
1 |
| adversarial learning (might be DL or ML, the details are not in the abstract, and there is no link to the paper) |
1 |
| classic SML |
1 |
| crowd sourcing |
1 |
| dependency parsing |
1 |
| dictionary approach, unsupervised machine learning |
1 |
| lexicon-based method, bag-of-words module and semantic module |
1 |
| natural language explanation framework |
1 |
| natural language processing (NLP) |
1 |
| rule-based (dependency parsing) |
1 |
| rule-based (keywords-in-context |
1 |
| semi-supervised SML |
1 |
| shallow supervised ML |
1 |
| text mining |
1 |
| weakly supervised ML |
1 |
| weakly supervised learning paradigm |
1 |
| zero-shot stance detection on Twitter that uses adversarial learning |
1 |